Fast and accurate identification of fat droplets in histological images.

ABSTRACT

BACKGROUND AND OBJECTIVE: The accurate identification of fat droplets is a prerequisite for the automatic quantification of steatosis in histological images. A major challenge in this regard is the distinction between clustered fat droplets and vessels or tissue cracks. METHODS: We present a new method for the identification of fat droplets that utilizes adjacency statistics as shape features. Adjacency statistics are simple statistics on neighbor pixels. RESULTS: The method accurately identified fat droplets with sensitivity and specificity values above 90%. Compared with commonly-used shape features, adjacency statistics greatly improved the sensitivity toward clustered fat droplets by 29% and the specificity by 17%. On a standard personal computer, megapixel images were processed in less than 0.05s. CONCLUSIONS: The presented method is simple to implement and can provide the basis for the fast and accurate quantification of steatosis.